code: def allreduce_ring_single_shard(
      xs, devices, reduction_fn_string="SUM"
  ):
      """Compute the reduction of all Tensors and put the result everywhere.
  
      Performance-optimized for a ring of devices.
  
      Args:
        xs: a list of n tf.Tensors
        devices: a list of strings
        reduction_fn_string: "SUM" or "MAX"
  
      Returns:
        a list of n Tensors
      Raises:
        ValueError: if devices is not a list of n strings
      """
      n = len(xs)
      binary_reduction = mtf.binary_reduction_fn(reduction_fn_string)
      assert (
          len(devices) == n
      ), "devices must be a list of length len(xs)"
      if n == 1:
          return xs
      result = [None] * n
      if n % 2 == 0:
          left_center = n // 2 - 1
          right_center = left_center + 1
      else:
          left_center = n // 2
          right_center = left_center
      left_sum = xs[0]
      for i in xrange(1, left_center + 1):
          with tf.device(devices[i]):
              left_sum = binary_reduction(left_sum, xs[i])
      right_sum = xs[n - 1]
      for i in reversed(xrange(left_center + 1, n - 1)):
          with tf.device(devices[i]):
              right_sum = binary_reduction(xs[i], right_sum)
      with tf.device(devices[left_center]):
          result[left_center] = binary_reduction(left_sum, right_sum)
      if n % 2 == 0:
          with tf.device(devices[right_center]):
              result[right_center] = binary_reduction(
                  left_sum, right_sum
              )
      for i in reversed(xrange(left_center)):
          with tf.device(devices[i]):
              result[i] = tf.identity(result[i + 1])
      for i in xrange(right_center + 1, n):
          with tf.device(devices[i]):
              result[i] = tf.identity(result[i - 1])
      return result
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import os
  import random
  from mesh_tensorflow import ops_with_redefined_builtins
  from six.moves import xrange
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling a global function with example usage: allreduce_ring_single_shard(xs, devices, reduction_fn_string) and returns: result, xs